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We are seeking a Staff Machine Learning Engineer to join our Foundation AI team. This team builds the multimodal foundation models that underpin WHOOP’s next generation of intelligent, personalized, and health-enhancing experiences. These models integrate data across wearable sensors, language, biomarkers, clinical information, and self-reported inputs to create scalable AI systems that understand human physiology and behavior. In this role, you’ll serve as a senior individual contributor driving the research, development, and deployment of large-scale multimodal models. You’ll collaborate closely with data scientists, ML engineers, and cross-functional partners to push the boundaries of deep learning and ensure our models deliver measurable value to WHOOP members.
Job Responsibility:
Design, train, and optimize large-scale multimodal foundation models that integrate wearable sensor data, text, biomarkers, and behavioral data
Conduct applied research in self-supervised learning, representation learning, and downstream task fine tuning to advance WHOOP’s core model capabilities
Develop scalable, distributed training pipelines for large models on high-performance compute environments
Collaborate with MLOps, data engineering, and software engineering teams to operationalize models for production deployment, ensuring robustness, reproducibility, and observability
Partner with product and research teams to translate foundation model capabilities into downstream features that deliver meaningful member value
Contribute to the technical roadmap and architectural direction for foundation model development at WHOOP
Serve as a technical mentor for other data scientists, sharing best practices in deep learning, large-scale training, and multimodal data integration
Ensure models adhere to WHOOP’s standards for ethical, transparent, and privacy-preserving AI
Requirements:
Advanced degree (Master’s or Ph.D.) in Computer Science, Machine Learning, Electrical Engineering, or a related field, or equivalent professional experience
7+ years of experience in applied ML, AI research, or large-scale modeling, with a track record of delivering production systems
Expertise in modern deep learning (e.g., transformers, state space models) and multimodal model training
Proficiency in Python and deep learning frameworks (e.g., PyTorch, TensorFlow)
Experience building and scaling large datasets and training large models in distributed compute environments
Strong applied experience with representation learning, self-supervised methods, and fine-tuning for downstream applications
Familiarity with MLOps best practices including model versioning, evaluation, CI/CD for ML, and cloud-based compute
Excellent communication skills and ability to collaborate cross-functionally with engineers, researchers, and product teams
Passion for WHOOP’s mission to improve human performance and extend healthspan through science and technology
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